Answers before you ask

A focused first release — one department, one core workflow — is typically eight to fourteen weeks. Full replacement of a multi-department platform runs six to twelve months. We deliberately ship the first useful slice early so you are evaluating working software within the first two months, not at the end.

Migration is planned as part of discovery, not as an afterthought. We profile the existing data first, agree what gets migrated versus archived, and run the migration repeatedly against a staging environment until it is clean. Cutover happens with a rehearsed rollback path.

Yes, entirely. The repository, the infrastructure configuration and the documentation are yours from the first commit. We hand over deployment access at the end of the engagement, and there is no licence, no escrow arrangement and no lock-in.

Frequently. We can lead the build with your engineers embedded, take a defined component while your team owns the rest, or run architecture and code review alongside an in-house team. The handover is smoother when your people were part of building it.

Support is a defined agreement, not a favour. Typical terms cover a response window for critical issues, a monthly allocation for enhancements, and quarterly reviews of performance and security. You can also take the system entirely in-house — the documentation is written for that outcome.

Most often Next.js and TypeScript on the front end with either .NET or Node.js behind it, over PostgreSQL or SQL Server. We choose per project — the deciding factors are your existing systems, your team's ability to maintain it, and what the workload actually demands.

Yes. We start with a two-week audit covering architecture, dependency health, security posture, test coverage and the highest-risk areas. You get a written assessment and a prioritised plan before committing to any development work.

Load testing during development establishes where the ceiling actually is, then autoscaling, connection pooling and caching are configured against that number. We would rather tell you the system handles 900 concurrent users than promise it handles 'a lot'.

Where it earns its place. Offline capability and installability are genuinely valuable for field teams and shop-floor use, and unnecessary overhead for an internal admin tool. We will tell you which case you are in.

Flutter for the majority: business apps, marketplaces, field tools, internal apps. Native when the product depends on deep platform integration — heavy background processing, complex camera or sensor work, platform-specific hardware. We make the call during discovery and explain the trade-off rather than defaulting to whichever is cheaper for us.

Yes, including developer account setup, signing certificates, store listings, privacy declarations and the review process. Rejections are usually caused by a missing privacy disclosure or an unclear data-usage statement — we prepare those up front.

We build it, or integrate with what you have. If the app is an extension of an existing system, we usually add a purpose-built mobile API layer rather than exposing the internal one directly — it keeps the app's payloads small and the internal system free to change.

Feature flags and remote configuration let us change behaviour without a store release, and the API is versioned so older app versions keep working. Users on old builds are a fact of mobile, not an exception to plan around later.

Often you should buy one — if your processes are close to standard, a licensed product is faster and cheaper. Building makes sense when your operating model is the competitive advantage, when licence costs scale painfully with headcount, or when you have already tried to configure a packaged system into something it was not designed to do. We will say so if buying is the better answer.

Module by module, usually starting with inventory or sales because they produce visible wins early, then finance, then the rest. Each module runs in parallel with the old process for at least one full cycle, and we do not switch off the old system until the numbers reconcile.

Yes. Some clients keep their existing accounting package and use the ERP for operations, with a posting integration between them. Others consolidate everything. Both are supported — the integration is designed during discovery.

Role-based training sessions, written procedures for each role, and a support period with a named contact during the first close. The most common cause of ERP failure is not software quality — it is people quietly reverting to spreadsheets in week three.

Yes. Multi-branch, multi-warehouse and multi-currency are designed in rather than added later, including inter-branch transfers, branch-level P&L and consolidated group reporting.

Yes — design-only engagements are common, and the handover is written for an engineering team we will never meet. That said, we will flag anything in the design that will be expensive to implement, which is easier to do when we know the stack.

We agree the metrics before starting: task completion time, error rate, drop-off at a specific step, support tickets on a particular screen. Baseline first, measure after. 'It looks better' is not a result.

Often, and usually incrementally. A full redesign shipped at once is risky and expensive; reworking the three highest-traffic flows first delivers most of the value and gives you real data before committing to the rest.

A Figma file with a token-based component library, documented flows, prototype links, a written research summary, and specifications covering states and edge cases — loading, empty, error, permission-denied — not just the happy path.

Azure most often, and AWS regularly. We also work with regional and on-premise hosting where data residency or contractual requirements demand it. The pipeline and infrastructure-as-code approach stays broadly the same across all of them.

Usually. Most gains come from a small number of changes: a real staging environment, an automated pipeline, tested backups and meaningful alerts. We start with an assessment and a prioritised list, and rebuilding is a last resort rather than an opening move.

Probably not. Kubernetes solves problems most businesses do not have, and adds operational complexity that then needs owning. Managed container services handle the large majority of workloads with far less overhead. We will recommend it only when the workload genuinely calls for it.

Dependency scanning, static analysis and secret detection run on every build, with secrets held in a managed vault rather than in environment files. Infrastructure changes go through the same review process as application code.

Usually. Options include database-level integration where access allows, scheduled file exchange, or screen-level automation as a last resort. We document the constraints honestly — some of these approaches are more fragile than others and you should know which you are buying.

It gets designed for. Requests queue, retry with exponential backoff, and surface on a dashboard if they exhaust their attempts. Where the business process allows, we degrade gracefully rather than blocking — an order can be accepted and the downstream notification retried.

Yes, including developer documentation, sandbox environments, API key management and usage analytics. A public API is a product with its own support obligations, and we plan it that way.

By agreeing a single system of record for each entity before any code is written, then syncing in one direction with reconciliation reporting in the other. Bidirectional sync without a defined owner is how data corruption starts.

Start with the assessment. It looks at your highest-volume manual tasks, estimates achievable accuracy and running cost, and produces a shortlist with payback periods. Sometimes the honest conclusion is that a well-built rules engine solves it for a tenth of the cost, and we will say so.

No. Where hosted models are used we select deployments with no-training guarantees and appropriate data residency. Where the data is sensitive enough, we run models in your own environment. This is agreed in writing before any data moves.

It depends entirely on the task and the quality of your data, which is why the pilot exists. Structured document extraction commonly lands in the mid-to-high nineties; open-ended classification is lower. We report measured accuracy on your data rather than quoting benchmark figures.

That is assumed rather than hoped against. Confidence thresholds route uncertain cases to human review, every automated decision is logged and reversible, and accuracy is monitored in production so drift is caught rather than discovered.